Import Dataset with read.csv

data <- read.csv("hr_analysis.csv")

head(data)
qaqc_plot <- ggplot() + geom_point(data=data, 
                                   aes(utm.easting,utm.northing,
                                       color=individual.local.identifier)) +
                        labs(x="Easting", y="Northing") +
                        guides(color=guide_legend("Identifier"))

ggplotly(qaqc_plot)
lapply(split(data, data$individual.local.identifier), 
       function(x)write.csv(x, file = paste(x$individual.local.identifier[1],".csv", sep = ""), row.names = FALSE))
files <- list.files(path = ".", pattern = "[PESU]+[0-9]+", full.names = TRUE)

1 Imagery

utm_points <- cbind(data$utm.easting, data$utm.northing)
utm_locations <- SpatialPoints(utm_points, 
                 proj4string=CRS("+proj=utm +zone=16 +datum=WGS84"))
proj_lat.lon <- as.data.frame(spTransform(
                utm_locations, CRS("+proj=longlat +datum=WGS84")))
colnames(proj_lat.lon) <- c("x","y")
raster <- openmap(c(max(proj_lat.lon$y)+0.005, min(proj_lat.lon$x)-0.05), 
                  c(min(proj_lat.lon$y)-0.005, max(proj_lat.lon$x)+0.05), 
                  type = "bing")
raster_utm <- openproj(raster, 
              projection = "+proj=utm +zone=16 +datum=WGS84 +units=m +no_defs")
autoplot.OpenStreetMap(raster_utm, expand = TRUE) + theme_bw() +
  theme(legend.position="bottom") +
  theme(panel.border = element_rect(colour = "black", fill=NA, size=1)) +
  geom_point(data=data, aes(utm.easting,utm.northing,
             color=individual.local.identifier), size = 3, alpha = 0.8) +
  theme(axis.title = element_text(face="bold")) + labs(x="Easting",
        y="Northing") + guides(color=guide_legend("Identifier"))

2 Home Range

2.1 MCP

mcp_raster <- function(filename){
  data <- read.csv(file = filename)
  x <- as.data.frame(data$utm.easting)
  y <- as.data.frame(data$utm.northing)
  xy <- c(x,y)
  data.proj <- SpatialPointsDataFrame(xy,data, proj4string = CRS("+proj=utm +zone=16 +datum=WGS84 +units=m +no_defs"))
  xy <- SpatialPoints(data.proj@coords)
  mcp.out <- mcp(xy, percent=100, unout="ha")
  mcp.points <- cbind((data.frame(xy)),data$individual.local.identifier)
  colnames(mcp.points) <- c("x","y", "identifier")
  mcp.poly <- fortify(mcp.out)
  units <- grid.text(paste(round(mcp.out@data$area,2),"ha"), x=0.85,  y=0.95,
                     gp=gpar(fontface=4, col="white", cex=0.9), draw = FALSE)
  mcp.plot <- autoplot.OpenStreetMap(rasterss_utm, expand = TRUE) + theme_bw() + theme(legend.position="none") +
    theme(panel.border = element_rect(colour = "black", fill=NA, size=1)) +
    geom_polygon(data=mcp.poly, aes(x=mcp.poly$long, y=mcp.poly$lat), alpha=0.8) +
    geom_point(data=mcp.points, aes(x=x, y=y)) + 
    labs(x="Easting (m)", y="Northing (m)", title=mcp.points$identifier) +
    theme(legend.position="none", plot.title = element_text(face = "bold", hjust = 0.5)) + 
    annotation_custom(units)
  mcp.plot
}
lapply(files, mcp_raster) 
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2.2 Kernel-Density Estimation

kde_raster <- function(filename){
  data <- read.csv(file = filename)
  x <- as.data.frame(data$utm.easting)
  y <- as.data.frame(data$utm.northing)
  xy <- c(x,y)
  data.proj <- SpatialPointsDataFrame(xy,data, proj4string = CRS("+proj=utm +zone=16 +datum=WGS84 +units=m +no_defs"))
  xy <- SpatialPoints(data.proj@coords)
  kde<-kernelUD(xy, h="href", kern="bivnorm", grid=100)
  ver <- getverticeshr(kde, 50)
  kde.points <- cbind((data.frame(data.proj@coords)),data$individual.local.identifier)
  colnames(kde.points) <- c("x","y","identifier")
  kde.poly <- fortify(ver)
  units <- grid.text(paste(round(ver$area,2)," ha"), x=0.85,  y=0.95,
                     gp=gpar(fontface=4, col="white", cex=0.9), draw = FALSE)
  kde.plot <- autoplot.OpenStreetMap(rasters_utm, expand = TRUE) + theme_bw() + theme(legend.position="none") +
    theme(panel.border = element_rect(colour = "black", fill=NA, size=1)) +
    geom_polygon(data=kde.poly, aes(x=kde.poly$long, y=kde.poly$lat), alpha = 0.8) +
    geom_point(data=kde.points, aes(x=x, y=y)) +
    labs(x="Easting (m)", y="Northing (m)", title=kde.points$identifier) +
    theme(legend.position="none", plot.title = element_text(face = "bold", hjust = 0.5)) + 
    annotation_custom(units)
  kde.plot
}
pblapply(files, kde_raster)
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3 Fun Message